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| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
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| import json | |
| import urlparse | |
| from itertools import chain | |
| flatten = chain.from_iterable | |
| from nltk import word_tokenize | |
| from gensim.corpora import Dictionary | |
| from gensim.models.ldamodel import LdaModel | |
| from gensim.models.tfidfmodel import TfidfModel |
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| import time | |
| from multiprocessing import Pool, TimeoutError | |
| import multiprocessing | |
| def run(input): | |
| name = multiprocessing.current_process().name | |
| print name, 'start' | |
| if input == 1000000-1: |

